Distinct spatio-temporal and spectral brain patterns for different thermal stimuli perception.

Understanding the human brain's perception of different thermal sensations has sparked the interest of many neuroscientists. The identification of distinct brain patterns when processing thermal stimuli has several clinical applications, such as phantom-limb pain prediction, as well as increasing the sense of embodiment when interacting with neurorehabilitation devices. Notwithstanding the remarkable number of studies that have touched upon this research topic, understanding how the human brain processes different thermal stimuli has remained elusive.

Sensory stimulation enhances phantom limb perception and movement decoding.

Objective: A major challenge for controlling a prosthetic arm is communication between the device and the user's phantom limb. We show the ability to enhance phantom limb perception and improve movement decoding through targeted transcutaneous electrical nerve stimulation in individuals with an arm amputation.

Approach: Transcutaneous nerve stimulation experiments were performed with four participants with arm amputation to map phantom limb perception.

Decoding of Pain Perception using EEG Signals for a Real-Time Reflex System in Prostheses: A Case Study.

In recent times, we have witnessed a push towards restoring sensory perception to upper-limb amputees, which includes the whole spectrum from gentle touch to noxious stimuli. These are essential components for body protection as well as for restoring the sense of embodiment. Notwithstanding the considerable advances that have been made in designing suitable sensors and restoring tactile perceptions, pain perception dynamics and its decoding using effective bio-markers, are still not fully understood.

Validating Deep Neural Networks for Online Decoding of Motor Imagery Movements from EEG Signals.

Non-invasive, electroencephalography (EEG)-based brain-computer interfaces (BCIs) on motor imagery movements translate the subject's motor intention into control signals through classifying the EEG patterns caused by different imagination tasks, e.g., hand movements.

Classification and regression of spatio-temporal signals using NeuCube and its realization on SpiNNaker neuromorphic hardware.

Objective: The objective of this work is to use the capability of spiking neural networks to capture the spatio-temporal information encoded in time-series signals and decode them without the use of hand-crafted features and vector-based learning and the realization of the spiking model on low-power neuromorphic hardware.

Approach: The NeuCube spiking model was used to classify different grasp movements directly from raw surface electromyography signals (sEMG), the estimations of the applied finger forces as well as the classification of two motor imagery movements from raw electroencephalography (EEG). In a parallel investigation, the designed spiking decoder was implemented on SpiNNaker neuromorphic hardware, which allows low-energy real-time processing.

Gumpy: a Python toolbox suitable for hybrid brain-computer interfaces.

Objective: The objective of this work is to present gumpy, a new free and open source Python toolbox designed for hybrid brain-computer interface (BCI).

Approach: Gumpy provides state-of-the-art algorithms and includes a rich selection of signal processing methods that have been employed by the BCI community over the last 20 years. In addition, a wide range of classification methods that span from classical machine learning algorithms to deep neural network models are provided.